A Review of the Relationship between Corporate Financial Performance and the Level of Related Party Transactions among Listed Companies on Tehran Stock Exchange
Bibliographic record
Abstract
The main objective of the investors to invest in stocks is to earn a profit and this is achieved by firm performance improvement. So the investors analyze various kinds of financial performance data for the different kinds of business models to determine whether some models perform better than others. The present study aims to collect the evidences of the relationship between firm economic performance and the level of related party transactions on Tehran Stock Exchange. So far, empirical evidences are not provided to reveal a clear picture of the reasons behind the related party transactions in Iran. In the case of opportunistic behavior in transactions, it is expected that the level of related party transactions has a relationship with economic performance variables. The research data have been collected over 1387-1393 for companies listed on Tehran Stock Exchange and to test the hypotheses, multivariate regression analysis of panel data is used. The results indicate that at a 95% confidence level, the economic value added (EVA), refined economic value added (Reva) and the market value added (MVA) variables have a significant relationship with the level of related party transactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".